From identifying cancerous cells to predicting disease progression, AI enables pathologists to make more informed decisions, improving patient care and outcomes. However, the success of AI algorithms relies heavily on high-quality annotated data. Is in-house data labeling the sole solution for maintaining data quality? Interestingly, The 2023 State of MLOps Report revealed that 55% of companies not outsourcing data labeling cited a lack of data quality as the top reason for their ML project failures. Moreover, nearly half of the survey respondents said outsourcing saves time, adds flexibility ..read more

In the rapidly evolving field of digital pathology, three key challenges demand attention as the realm of AI and computational power expands. These challenges revolve around enhancing laboratory operations, providing clinical decision support, and advancing research and development. With artificial intelligence algorithms and high-quality training data for AI models, pathology diagnostics AI can improve accuracy, cost-effectiveness, and optimal patient care. In this blog post, we will explore the applications of AI in pathology and examine how high-quality data and data annotation play a cruci ..read more

Technological advances and the increased focus on precision medicine have recently paved the way for developing digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)–based solutions, allowing us to explore and extract information beyond human visual perception. Today, we are past the point of early adoption. There will be an inflection point where AI suddenly becomes a standard part of routine diagnostic practice.  By analyzing large volumes of data and identifying patterns that may be difficult for human ex ..read more

The success of your AI projects is determined by the model performance, which, in turn, depends on the quality of annotated training dataset fed to the model. Data annotation is a time-consuming, expensive, and painful part of any AI project, requiring heavy investments and resources. Many organizations rely on data annotation tools to label and enrich data for training.  A report by Grand View Research suggests that the size of the global data annotation tools market was USD 806 million in 2022, growing at a CAGR of 26% from 2023-2030. One of the benefits of a data annotation tool is tha ..read more

With AI becoming a critical aspect of businesses and over 77% of devices worldwide using it in one form or the other, the global AI market will reach $90 billion by 2025. Another study suggests that 80% of businesses will need AI and machine learning operations by next year.  The surging adoption of AI/ML models is mainly due to the efficiencies they offer businesses, yet they still rely on human intelligence and input for training. The data fed into AI models dictates their accuracy, and it is important to recognize that human involvement is indispensable throughout the process. Whether ..read more

As we navigate the world of autonomous mobility, we are all in the midst of a significant learning process. The future of autonomous driving systems depends on integrating many technologies to promote the safety, comfort, and acceptance of autonomous driving systems. These technologies include high-definition mapping and street localization. HD maps have progressed massively over the last few years, yet they are not close to the precision required for autonomous driving. On the other hand, developing countries do not have HD mapping products or technologies at all, which makes it even more ch ..read more

HD maps provide autonomous vehicles with all the useful information like intersection width, traffic signal location, the height of a structure, the speed limit, etc. With inch-perfect accuracy and high environmental fidelity – HD Maps contain exact positions of pedestrian crossings, traffic lights/signs, barriers, and more.  Every time a self-driving car enters a new city or a town, the sensor and lasers on it help draw a 3D visualization of the place. For example, distance is measured by the time the laser beam takes to go to the object and bounce back to the car sensor. Sensors can als ..read more

The market for autonomous vehicles is evolving rapidly, with numerous companies developing cutting-edge technology to enable self-driving technology to navigate roads safely and efficiently. Unlike human drivers, autonomous cars need to learn how to drive, read maps, and arrive at their destination using real-time data and through interaction with the surroundings.  For building a safe and secure autonomous mobility technology, you will need high-quality HD maps for precise navigation that are updated regularly. High-Definition (HD) Maps are essential for developing autonomous vehicles as ..read more

The Robotaxi market globally is expected to grow at a CAGR of 137% from 2021 to 2030, with vehicle units increasing from 617 to 1,445,822, as per a report by MarketsandMarkets. Fuel efficiency, emission- control, road safety, and traffic management are a few factors driving its growth. The surging demand of the segment requires Robotaxi companies to improve the safety of their autonomous vehicles, which depends on the performance of their ML models.  While developers at Robotaxi companies are improving and refining their models, they need to know how accurate the ground truth data is. iMe ..read more

YOLO (You Only Look Once) is a fast and effective deep neural network (DNN) architecture that can identify and locate multiple objects in video, in real time. YOLO is a great example of innovative architectural elements combined to create a state-of-the-art machine learning system, in this case one for computer vision applications such as autonomous driving. The original version of this real-time object detection algorithm was developed in 2015 and described in You only look once: unified, real-time object detection, a paper by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. Al ..read more